Canadian Forest Service Publications

Land surface phenology from optical satellite measurement and CO₂ eddy covariance technique. 2012. Gonsamo, A.; Chen, J.M.; Price, D.T.; Kurz, W.A.; Wu, Chaoyang. Journal of Geophysical Research 117(G3):1-18.

Year: 2012

Issued by: Northern Forestry Centre

Catalog ID: 34167

Language: English

Availability: Order paper copy (free), PDF (request by e-mail)

Available from the Journal's Web site. †
DOI: 10.1029/2012JG002070

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Abstract

Land surface phenology (LSP) is an integrative indicator of vegetation dynamics under a changing environment. Increasing amounts of remote sensing measurements and CO2 flux observations offer unprecedented opportunities to quantify LSP phases at landscape scale. LSP start of season (SOS) and end of season (EOS) estimates are often based on the use of a single-purpose vegetation index derived from optical satellite data, characterized by poor performances in decoupling soil and snow cover dynamics from LSP cycles, as well as contrasting responses of the needleleaf and broadleaf forests in boreal ecosystems. We propose a new remote-sensing-based phenology index (PI) which combines the merits of normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) by taking the difference of squared greenness and wetness to remove the soil and snow cover dynamics from key vegetation LSP cycles. We have cross-validated the remote-sensing-based LSP dates with those of CO2 flux observations from 11 selected tower sites across Canada and the United States consisting of needleleaf forests, boadleaf forests, and croplands. The results indicate that PI estimates the SOS and EOS dates better than NDVI when compared to the LSP dates from CO2 flux measurements (reduced RMSE, bias and dispersions, and higher correlation). PI-based SOS and EOS estimates are in good agreement with those derived from CO2 flux measurements with mean bias comparable to the temporal resolution of the high-quality, 8-day composite satellite measurements. Finally, PI also shows a smoother time series compared to NDVI and NDII.